[PDF] - Autonomic Computing Introduction, Motivations, Overview Manish PDF Document

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Autonomic Computing Introduction, Motivations, Overview

Manish Parashar The Applied Software Systems Laboratory Rutgers, The State University of New Jersey http://automate.rutgers.edu Salim Hariri High Performance Distributed Computing Laboratory The University of Arizona http://www.ece.arizona.edu/~hpdc ICAC 2004 Autonomic Computing Tutorial May 16, 2004

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Tutorial Outline

Objectives

– lay the foundations of Autonomic Computing – present the defining research issues, present the

pportunities and challenges of Autonomic Computing

– review the current landscape of Autonomic Computing – present an overview of AutoMate and Autonomia

More Information

– http://www.autonomic-conference.org/tutorial/ – http://automate.rutgers.edu/

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Agenda

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Emerging Information Infrastructures - Smaller/Cheaper/Faster/Powerful/Connected ….

Explosive growth in computation, communication,

information and integration technologies

– computing & communication is ubiquitous

Pervasive ad hoc “anytime-anywhere” access

environments

– ubiquitous access to information – peers capable of producing/consuming/processing information at different levels and granularities – embedded devices in clothes, phones, cars, mile-markers, traffic lights, lamp posts, medical instruments …

“On demand” computational/storage resources, services

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Faster/Smaller/Cheaper/Powerful/Connected ….

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Motivation: Complexity

Directory Directory and Security and Security Services Services Existing Existing Applications Applications and Data and Data Business Business Data Data

Data Data Server Server Web Web Application Application Server Server

Storage Area Storage Area Network Network BPs and BPs and External External Services Services

Web Web Server Server DNS DNS Server Server

Data Data

Dozens of systems and applications Hundreds of components Thousands of tuning parameters

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Motivation: Complexity

Individual system elements increasingly difficult to maintain and operate

– 100s of config, tuning parameters for commercial databases, servers, storage

Heterogeneous systems are becoming increasingly connected

– Integration becoming ever more difficult

Architects can't intricately plan component interactions

– Increasingly dynamic; more frequently with unanticipated components

This places greater burden on system administrators, but

– they are already overtaxed – they are already a major source of cost (6:1 for storage) and error

We need self-managing computing systems

– Behavior specified by sys admins via high-level policies – System and its components figure out how to carry out policies

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Motivation: Increasing Cost

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Rapid Changes, I ncreased Complexity

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The bad news …

Unprecedented

– scales, complexity, heterogeneity, dynamism and unpredictability, lack of guarantees

Millions of businesses, Trillions of devices, Millions of developers and

users, Coordination and communication between them

The increasing system complexity is reaching a level

beyond human ability to design, manage and secure

– programming environments and infrastructure are becoming unmanageable, brittle and insecure

A fundamental change is required in how system and

applications are formulated, constructed, composed and managed

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Convergence of Information Technology and Biology

Our system design methods and management tools

seem to be inadequate for handling the complexity, size, and heterogeneity of today and future Information systems

Biological systems have evolved strategies to cope with

dynamic, complex, highly uncertain constraints

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Adaptive Biological Systems

The body’s internal mechanisms

continuously work together to maintain essential variables within physiological limits that define the viability zone

Two important observations:

– The goal of the adaptive behavior is directly linked with the survivability – If the external or internal environment pushes the system

utside

its physiological equilibrium state the system will always work towards coming back to the original equilibrium state

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Ashby’s Ultrastable System

The Ashby Ultra-Stable system consists as two close loops:
ne that can control small disturbances while the second

control loop is responsible for longer disturbances.

Reacting Part R Environment Step Mechanisms/Input Parameter S Essential Variables Motor channels Sensor channels

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The Nervous System: A subsystem within Ashby’s Ultrastable System

The nervous system is divided into the Peripheral Nervous

System (PNS) and the Central Nervous System (CNS)

CNS consists of two parts: sensory-somatic nervous system and

the autonomic nervous system.

S = f (change in EV) Internal environment External environment

Reacting Part R

Sensory Neurons Motor Neurons

Sensor Channels Motor Channels Environment

Essential Variables Step Mechanisms/Input Parameter S (EV)

Central nervous system (CNS) External environment Internal environment Sensory neurons Sensory neurons Motor neurons Motor neurons Autonomic Nervous System Sensory – Somatic Nervous System

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Convergence of Information Technology and Biology

Without requiring our conscious involvement

when we run, it increases
ur heart and breathing

rate

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Autonomic Computing?

Nature has evolved to cope with scale, complexity, heterogeneity,

dynamism and unpredictability, lack of guarantees

– self configuring, self adapting, self optimizing, self healing, self protecting, highly decentralized, heterogeneous architectures that work !!! – e.g. the human body – the autonomic nervous system

tells you heart how fast to beat, checks your blood’s sugar and oxygen

levels, and controls your pupils so the right amount of light reaches your eyes as you read these words, monitors your temperature and adjusts your blood flow and skin functions to keep it at 98.6ºF

coordinates - an increase in heart rate without a corresponding adjustment

to breathing and blood pressure would be disastrous

is autonomic - you can make a mad dash for the train without having to

calculate how much faster to breathe and pump your heart, or if you’ll need that little dose of adrenaline to make it through the doors before they close

– can these strategies inspire solutions?

e.g. FlyPhones, AORO/AutoMate, ROC, ELiza, etc.

– of course, there is a cost

lack of controllability, precision, guarantees, comprehensibility, …

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Autonomic Computing – The Next Era of Computing “ Computer Systems that can regulate themselves much in the same way as our autonomic nervous system regulates and protects our bodies.”

(by Paul Horn, IBM)

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Autonomic Computing - The Vision “ increasing productivity while minimizing complexity for users… ” “ to design and build computing systems capable of running themselves, adjusting to varying circumstances, and preparing their resources to handle most efficiently the workloads we put upon them. “

By IBM

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PS: Its not AI

Does not require the duplication of conscious human

thought as an ultimate goal.

Does require system to take over certain functions

previously performed by humans

By IBM

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Autonomic Computing Characteristics (IBM)

1. Self Defining

– To be autonomic, a computing system needs to know itself and comprise components – It needs detail knowledge of its components, current state, ultimate capacity – It needs to know all the connections to other systems to govern itself – It needs to know ownership level, from whom it can borrow resources, share or not to share, etc.

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Autonomic Computing Characteristics (IBM)

By IBM

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Autonomic Computing Characteristics (IBM)

Self Awareness

Possesses a sense of self and strive to improve its performance

Context Aware

Anticipates users actions and are aware of the context

Open

Communicates through open standards and can exchange resources with unfamiliar systems

Self Regulating

Possesses a sense of self discipline and can regulate its behavior according to the changes in its environment

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Autonomic Computing Characteristics (IBM)

6. Contextually Aware

– It must know its environment and the surrounding context of its activity – It will find and generate rules for how best to interact with neighboring systems – How to access available resources, negotiate usage deals/contracts

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Autonomic Computing Characteristics (IBM)

7. Open

– Must function in a heterogeneous environment and implement

pen standards

– It must coexist and depend upon one another for survivable (people connect to banks, travel agents, department stores regardless of the underlying software/hardware technologies used to implement these services

8. Anticipatory

– Ability to anticipate workflow challenges and optimize system for immediate user needs

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Application Scenarios

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By IBM

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By IBM

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Autonomic Platform (Pervasive Application)

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Autonomic Living

Autonomic living: autonomic peers opportunistically interact,

coordinate and collaborate to satisfy goals?

– scenarios (everyday, b2b coordination, crisis management, homeland security, …)

your car in route to the airport estimates that given weather (from

meteorological beacons), road conditions (from on-coming cars), traffic patters (from the traffic light), warns that you will miss your flight and you will be better off taking the train – the station is coming up – do you want to rebook ?

in a foreign country, your cell phone enlists a locally advertised GPS

and translation service as you try to get directions

your clock/PDA estimates drive time to your next appointment and

warns you appropriately

your eye glasses sends your current prescription as you happen to drive

past your doctor or your PDA collects prices for the bike you promised yourself as you drive around

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Scope of Autonomic Computing (IBM)

Holistic approach

– Automation and manageability enablement at each system layer – Federated heterogeneous components interacting cohesively

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Structure of Autonomic Computing (IBM)

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Evolution towards Self Management (IBM)

Automated defense against malicious attacks or cascading failures; use early warning to anticipate and prevent system-wide failures. Manual vulnerability analysis. Manual detection and recovery from attacks, cascading failures.

Protect

Components and systems will continually strive to improve their own performance and efficiency. Web servers, databases have hundreds of nonlinear tuning parameters; many new ones with each release. Adjusted manually.

Optimize

Automated detection, diagnosis, and repair of localized software/hardware problems. Problem determination in large, complex systems can take a team of programmers weeks.

Heal

Automated configuration of components, systems according to high-level policies; rest of system adjusts seamlessly. Corporate data centers are multi- vendor, multi-platform. Installing, configuring, integrating systems is time-consuming, error-prone.

Configure The Autonomic Future The Human-Intensive Present Self-

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Evolving towards Autonomic Computing Systems (IBM)

Manual Autonomic

Benefits Skills Characteristics

Level 1 Level 2 Level 3

Multiple sources of system generated data Extensive, highly skilled IT staff Basic Requirements Met Data & actions consolidated through mgt tools IT staff analyzes & takes actions Greater system awareness Improved productivity Sys monitors correlates & recommends actions IT staff approves & initiates actions Less need for deep skills Faster/better decision making Sys monitors correlates & takes action IT staff manages performance against SLAs Human/system interaction IT agility & resiliency

Level 5

Components dynamically respond to business policies IT staff focuses

n enabling

business needs Business policy drives IT mgt Business agility and resiliency

Level 4

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How Do Design Autonomic Computing Systems

Grand Research Challenges

– The challenges are greater than any organization/company – It requires collaboration between leading labs, and cross- industry cooperation on standards and funding university research programs – There has been some serious steps taken toward Autonomic Computing, lead by IBM

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Autonomic Computing Architecture

Based on distributed, component/service-oriented

architectural approach

– Components both provide and consume services

Autonomic elements (components/services)

– Responsible for policy-driven self-management of individual components

Relationships among autonomic elements

– Based on agreements established/maintained by autonomic elements – Governed by policies – Give rise to resiliency, robustness, self-management of system

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Autonomic Elements: Structure

Fundamental atom of the

architecture

– Managed element(s)

Database, storage system,

server, software app, etc.

– Plus one autonomic manager

Responsible for:

– Providing its service – Managing its own behavior in accordance with policies – Interacting with other autonomic elements An Autonomic Element Managed Element

E S Monitor Analyze Execute Plan Knowledge

Autonomic Manager

J. Kephart, IBM, USA

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Autonomic Elements: Interactions

Relationships

– Dynamic, ephemeral – Defined by rules and constraints – Formed by agreement

May be negotiated

– Full spectrum

Peer-to-peer
Hierarchical

– Subject to policies

J. Kephart, IBM, USA

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Autonomic Elements: Composition of Autonomic Systems

Reputation Authority Network Registry Event Correlator Database Monitor Server Workload Manager Server Server Storage Storage Storage Negotiator Broker Provisioner Sentinel Monitor Aggregator Registry Monitor Broker Sentinel Arbiter Planner Workload Manager Database Network

J. Kephart, IBM, USA

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Autonomic Computing – Conceptual Architecture

A

S

High Performance Computing Environment

Internal External Environment

Control

E E KE autonomic behavior

cardinal M & A Cardinals

PE M&

G

E KE programmed behavior PE L A Autonomic Computing Tutorial, ICAC 2004 42

Autonomic Computing Framework

Application Management Editor User’s Application Specifications

Event Server

Monitoring &Analysis Engine Monitoring &Analysis Engine Scheduling Engine Scheduling Engine Planning Engine Planning Engine

AIK Repository

ACA Specifications
Policy Repository
Component Repository
Resource Repository

Policy Engine

Self Protecting Self Optimizing Self Healing Self Configuring

Autonomic Runtime System (ARS)

VEE1 VEEn VEE2

…

Application Runtime Manager (ARM)

ACAm ACA2

CRM

Computational Component

ACA1

CCA1 ACAm ACA2

CRM

Computational Component

ACA1

CCA2 ACAm ACA2

CRM

Computational Component

ACA1

CCA3

Coordinator

Knowledge Engine Knowledge Engine

Autonomic Middleware Services (AMS)

High Performance Computing Environment (HPCE)

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Autonomic Computing: Implementation Mechanisms

Self-Configure

Clusters
Upgrades
COD

Self-Optimize

Partitions
Workload

Balancing

Self-Healing

Failover
Rerouting

Self-Protection

Security
Encryption

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